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Author:

Zhang, Yun (Zhang, Yun.) | Qiao, Ai-Ke (Qiao, Ai-Ke.) (Scholars:乔爱科)

Indexed by:

EI Scopus PKU CSCD

Abstract:

Objective: To optimize the baseline on the trapezoidal cross-section of stent wires, so as to reduce the risk of intracranial saccular aneurysm rupture after the implantation of such stents. Methods: Thirty-eight trapezoidal cross-section wire stents with different baselines were constructed to establish the finite element models. Numerical simulation by fluid-solid interaction method was conducted to calculate 38 maximum pressure gradients on the aneurysm wall. GRNN (general regression neural network) and GA (genetic algorithm) were used to optimize the baseline on the cross-section of stents with trapezoidal cross-section wire so as to minimize the maximal pressure gradient on the aneurysm wall. Results: Compared with the traditional stent with rectangular cross-section wire, the maximal pressure gradient on the a neurysm wall was reduced by 7.86% after the implantation with the optimized stent with trapezoidal cross-section wire. Conclusions: The combination of GRNN and GA is an effective approach for stent optimization.

Keyword:

Pressure gradient Genetic algorithms Numerical models Neural networks Numerical methods Computer simulation Finite element method Wire Stents

Author Community:

  • [ 1 ] [Zhang, Yun]School of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Qiao, Ai-Ke]School of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Medical Biomechanics

ISSN: 1004-7220

Year: 2012

Issue: 3

Volume: 27

Page: 294-298

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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